Runtime function instrumentation with EZTrace

International audienceHigh-performance computing relies more and more on complex hardware: multiple computers, multi-processor computer, multi-core processing unit, multiple general purpose graphical processing units... To efficiently exploit the power of current computing architectures, modern applications rely on a high level of parallelism. To analyze and optimize these applications, tracking the software behavior with minimum impact on the software is necessary to extract time consumption of code sections as well as resource usage (e.g., network messages). In this paper, we present a method for instrumenting functions in a binary application. This method permits to collect data at the entry and the exit of a function, allowing to analyze the execution of an application. We implemented this mechanism in \eztrace and the evaluation shows a significant improvement compared to other tools for instrumentation

EZTrace: a generic framework for performance analysis

Poster SessionInternational audienceModern supercomputers with multi-core nodes enhanced by accelerators, as well as hybrid programming models, introduce more complexity in modern applications. Exploiting efficiently all the resources requires a complex analysis of the performance of applications in order to detect time-consuming or idle sections. We present eztrace, a generic trace generation framework that aims at providing a simple way to analyze applications. eztrace is based on plugins that allow it to trace different programming models such as MPI, pthread or OpenMP as well as user-defined libraries or applications. This framework uses two steps: one to collect the basic information during execution and one post-mortem analysis. This permits tracing the execution of applications with low overhead while allowing to refine the analysis after the execution of the program. We also present a simple script language for \eztrace that gives the user the opportunity to easily define the functions to instrument without modifying the source code of the application

Deep Markov Random Field for Image Modeling

Markov Random Fields (MRFs), a formulation widely used in generative image modeling, have long been plagued by the lack of expressive power. This issue is primarily due to the fact that conventional MRFs formulations tend to use simplistic factors to capture local patterns. In this paper, we move beyond such limitations, and propose a novel MRF model that uses fully-connected neurons to express the complex interactions among pixels. Through theoretical analysis, we reveal an inherent connection between this model and recurrent neural networks, and thereon derive an approximated feed-forward network that couples multiple RNNs along opposite directions. This formulation combines the expressive power of deep neural networks and the cyclic dependency structure of MRF in a unified model, bringing the modeling capability to a new level. The feed-forward approximation also allows it to be efficiently learned from data. Experimental results on a variety of low-level vision tasks show notable improvement over state-of-the-arts.Comment: Accepted at ECCV 201

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1 vista publicada a Paris aproximadament al 1860 i reproduida dins del diccionari. - [Títol original: Barcelona: vista tomada desde encima del recodo de Mataró y del Norte

L’Espagne a vol d’oiseau. Madrid. Vue prise au-dessus de la place des Taureaus

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1 vista publicada a Paris aproximadament al 1860 i reproduida dins del diccionari. - [Títol original: Barcelona: vista tomada desde encima del recodo de Mataró y del Norte

L’Espagne a vol d’oiseau. Madrid. Vue prise au-dessus de la place des Taureaus

1 Vist